PCR enrichment analysis
Kent Riemondy RBI
R Sys.Date()
Examine PCR enrichment data
Organize single cell libraries
First designate the libraries and the cells that were resampled.
libs <- list("original",
"standard",
"biotinylated",
"phosphorothioate")
cells <- list(
standard = c(
"Cell_4Bone_63_70",
"Cell_4Bone_27_61",
"Cell_4Bone_34_48",
"Cell_1Tumor_0_55",
"Cell_1Tumor_7_50",
"Cell_4Bone_30_23",
"Cell_4Bone_61_22",
"Cell_4Bone_66_9",
"Cell_3Brain_47_38",
"Cell_3Brain_14_46"
),
biotinylated = c(
"Cell_4Bone_63_70",
"Cell_4Bone_27_61",
"Cell_4Bone_34_48",
"Cell_1Tumor_0_55",
"Cell_1Tumor_7_50",
"Cell_3Brain_47_38",
"Cell_3Brain_14_46",
"Cell_4Bone_28_54",
"Cell_4Bone_26_20",
"Cell_4Bone_60_43"
),
phosphorothioate = c(
"Cell_1Tumor_7_50",
"Cell_3Brain_47_38",
"Cell_4Bone_28_54",
"Cell_4Bone_26_20",
"Cell_4Bone_60_43"
)
)
bc_metadat <- read_tsv(file.path(data_dir,
"original",
"fastq",
"original",
"well_data_barcode_keys.txt"),
col_names = c("cell_id", "barcode_10x"))
## original library to compare against
reflib <- "original"
resampled_libs <- c(
"standard",
"biotinylated",
"phosphorothioate")
## reference resampled lib for resampled vs control plots
resampled_lib <- "phosphorothioate"
## pretty name for libraries
lib_names = c(
original = "Original Library",
standard = "PCR",
biotinylated = "PCR +\n5' Biotin",
phosphorothioate = "PCR +\n5' Biotin +\n3' Phosphorothioates"
)
## pretty names for cells
cell_names <- list(
standard = c(
"Cell_4Bone_63_70" = "Bone Met Cell #4",
"Cell_4Bone_27_61" = "Bone Met Cell #5",
"Cell_4Bone_34_48" = "Bone Met Cell #6",
"Cell_1Tumor_0_55" = "Primary Tumor Cell #2",
"Cell_1Tumor_7_50" = "Primary Tumor Cell #1",
"Cell_4Bone_30_23" = "Bone Met Cell #7",
"Cell_4Bone_61_22" = "Bone Met Cell #8",
"Cell_4Bone_66_9" = "Bone Met Cell #9",
"Cell_3Brain_47_38" = "Brain Met Cell #1",
"Cell_3Brain_14_46" = "Brain Met Cell #2"
),
biotinylated = c(
"Cell_4Bone_63_70" = "Bone Met Cell #4",
"Cell_4Bone_27_61" = "Bone Met Cell #5",
"Cell_4Bone_34_48" = "Bone Met Cell #6",
"Cell_1Tumor_0_55" = "Primary Tumor Cell #2",
"Cell_1Tumor_7_50" = "Primary Tumor Cell #1",
"Cell_3Brain_47_38" = "Brain Met Cell #1",
"Cell_3Brain_14_46" = "Brain Met Cell #2",
"Cell_4Bone_28_54" = "Bone Met Cell #1",
"Cell_4Bone_26_20" = "Bone Met Cell #2",
"Cell_4Bone_60_43" = "Bone Met Cell #3"
),
phosphorothioate = c(
"Cell_1Tumor_7_50" = "Primary Tumor Cell #1",
"Cell_3Brain_47_38" = "Brain Met Cell #1",
"Cell_4Bone_28_54" = "Bone Met Cell #1",
"Cell_4Bone_26_20" = "Bone Met Cell #2",
"Cell_4Bone_60_43" = "Bone Met Cell #3"
)
)Load and organize a table for each library of read counts per cell per gene, and a table of umi counts per cell per gene.
umis_to_genes <- function(umipath, cells_to_exclude = c("Cell_unmatched")){
umis <- read_tsv(umipath,
col_names = c("barcode_10x",
"umi_molecule",
"count")) %>%
filter(barcode_10x != cells_to_exclude)
mol_fields <- str_count(umis$umi_molecule[1], "::")
if(mol_fields == 2 ){
umis <- separate(umis, umi_molecule,
into = c("seq", "genome", "gene"),
sep = "::") %>%
mutate(gene = str_c(genome, "::", gene))
} else if (mol_fields == 1){
umis <- separate(umis, umi_molecule,
into = c("seq", "gene"),
sep = "::")
} else {
stop("separator :: missing from umi_molecule field")
}
reads <- select(umis,
barcode_10x,
gene,
count)
reads <- group_by(reads,
barcode_10x, gene) %>%
summarize(counts = sum(count))
reads <- spread(reads, barcode_10x, counts,
fill = 0L)
reads
}
## read in umigroups flat file with read counts per umi per gene per cell
## expand out to a read count matrix
umipaths <- file.path(data_dir,
libs,
"umis",
"umigroups.txt.gz")
read_dat <- map(umipaths,
~umis_to_genes(.))
names(read_dat) <- libs
## read in umi_tools count table with umi counts per gene per cell
umi_dat <- map(libs,
~read_tsv(file.path(data_dir,
.x,
"dgematrix",
"dge_matrix.txt")) %>%
select(-matches("Cell_unmatched")))
names(umi_dat) <- libs
all_cells <- c(list(original = unlist(cells) %>% unname()),
cells)
cell_obj_mdata <- map(all_cells,
~mutate(bc_metadat,
resampled = ifelse(cell_id %in% .x,
TRUE,
FALSE)))Next organize these tables into simple classes called resampled-sets to keep track of each experiment’s relavant raw, processed, and meta data.
#' simple class to hold info for each experiment
create_sc_obj <- function(umi_df,
read_df,
cell_mdata_df){
x <- list()
class(x) <- "resampled-set"
x$umis <- umi_df
x$reads <- read_df
x$meta_dat <- cell_mdata_df
return(x)
}
sc_objs <- list(umi_dat, read_dat, cell_obj_mdata)
sc_objs <- pmap(sc_objs, create_sc_obj)
rm(umi_dat)
rm(read_dat)Next perform basic processing. 1) generate separate objects to store sparse matrices of umi and read counts. 2) normalize read and umi count data by total library size (sum of all read or umi counts for all cells in the experiment) and report as Reads per million or UMIs per million. 3) Compute per cell metrics (read and umi counts, sequencing saturation)
tidy_to_matrix <- function(df){
df <- as.data.frame(df)
rownames(df) <- df[, 1]
df[, 1] <- NULL
mat <- as.matrix(df)
mat <- as(mat, "sparseMatrix")
return(mat)
}
#' keep both tidy and matrix objs
generate_matrices <- function(sc_obj){
sc_obj$umi_matrix <- tidy_to_matrix(sc_obj$umis)
sc_obj$read_matrix <- tidy_to_matrix(sc_obj$reads)
sc_obj
}
#' normalize by library size (Reads per Million)
norm_libsize <- function(sc_obj){
sc_obj$norm_umi <- 1e6 * sweep(sc_obj$umi_matrix, 2,
sum(as.vector(sc_obj$umi_matrix)), "/")
sc_obj$norm_reads <- 1e6 * sweep(sc_obj$read_matrix, 2,
sum(as.vector(sc_obj$read_matrix)), "/")
sc_obj
}
add_metadata <- function(sc_obj, dat){
if (is.vector(dat)){
new_colname <- deparse(substitute(dat))
df <- data_frame(!!new_colname := dat)
df[[new_colname]] <- dat
df[["cell_id"]] <- names(dat)
sc_obj$meta_dat <- left_join(sc_obj$meta_dat,
df,
by = "cell_id")
} else if (is.data.frame(dat)) {
sc_obj$meta_dat <- left_join(sc_obj$meta_dat,
dat,
by = "cell_id")
}
sc_obj
}
compute_summaries <- function(sc_obj){
## raw counts
total_umis <- colSums(sc_obj$umi_matrix)
names(total_umis) <- colnames(sc_obj$umi_matrix)
total_reads <- colSums(sc_obj$read_matrix)
names(total_reads) <- colnames(sc_obj$read_matrix)
## norm counts
norm_total_umis <- colSums(sc_obj$norm_umi)
names(norm_total_umis) <- colnames(sc_obj$norm_umi)
norm_total_reads <- colSums(sc_obj$norm_reads)
names(norm_total_reads) <- colnames(sc_obj$norm_reads)
sc_obj <- add_metadata(sc_obj, total_umis)
sc_obj <- add_metadata(sc_obj, total_reads)
sc_obj <- add_metadata(sc_obj, norm_total_umis)
sc_obj <- add_metadata(sc_obj, norm_total_reads)
## compute cDNA duplication rate
sc_obj$meta_dat$cDNA_duplication <- 1 - (sc_obj$meta_dat$total_umis /
sc_obj$meta_dat$total_reads)
sc_obj
}
sc_objs <- map(sc_objs, generate_matrices)
sc_objs <- map(sc_objs, norm_libsize)
sc_objs <- map(sc_objs, compute_summaries)Compute enrichment of reads/umis over the original library.
sc_objs <- map(sc_objs,
function(sub_dat){
og_counts <- select(sc_objs[[reflib]]$meta_dat,
og_total_reads = total_reads,
og_total_umis = total_umis,
og_norm_total_umis = norm_total_umis,
og_norm_total_reads = norm_total_reads,
og_cDNA_duplication = cDNA_duplication,
cell_id)
sub_dat$meta_dat <- left_join(sub_dat$meta_dat,
og_counts,
by = "cell_id")
sub_dat$meta_dat <- mutate(sub_dat$meta_dat,
read_proportion = log2( total_reads / og_total_reads),
umi_proportion = log2( total_umis / og_total_umis),
norm_read_proportion = log2( norm_total_reads /
og_norm_total_reads),
norm_umi_proportion = log2( norm_total_umis /
og_norm_total_umis))
sub_dat
})plot cDNA duplication rate
sc_metadat <- map(sc_objs, ~.x$meta_dat) %>%
bind_rows(.id = "library") %>%
mutate(library = factor(library, levels = libs)) %>%
arrange(resampled)
plt <- ggplot(sc_metadat, aes(total_umis, cDNA_duplication)) +
geom_point(aes(color = resampled), size = 0.5) +
labs(x = expression(paste("# of UMIs")),
y = "Sequencing saturation") +
scale_x_log10(labels = scales::comma) +
scale_color_manual(values = color_palette) +
guides(colour = guide_legend(override.aes = list(size = 5))) +
facet_wrap(~library,
labeller = labeller(library = lib_names)) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(legend.position = "top",
plot.margin = unit(c(5.5, 20.5, 5.5, 5.5),
"points"))
plt
Note the high level of sequencing saturation (0 = no-duplication, 1 = all duplicates) in the original library. Also note that the libraries tend to have higher saturatioin rates, after subsampling.
save_plot("cDNA_duplication.pdf", plt,
base_height = 5.5,
base_aspect_ratio = 0.85)plot read and umi counts per library
global_plot_theme <- theme(
legend.position = "top",
legend.text = element_text(size = 10),
strip.text = element_text(size = 8))
resampled_metadat <- filter(sc_metadat,
library != "original") %>%
mutate(library = factor(library,
levels = c(
"standard",
"biotinylated",
"phosphorothioate")))
unnorm_plt <- ggplot(resampled_metadat,
aes(og_total_reads / 3, total_reads / 3, colour = resampled)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
facet_wrap(~library, nrow = 1) +
coord_fixed() +
xlab("original library\nreads count (Thousands)") +
ylab("resampled library\nreads count (Thousands)") +
# ggtitle("Raw reads associated with each cell barcode") +
scale_colour_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 10, line_size = .5) +
global_plot_theme
norm_plt <- ggplot(resampled_metadat, aes(og_norm_total_reads / 1e3, norm_total_reads / 1e3, colour = resampled)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
facet_wrap(~library, nrow = 1) +
xlab("original library\nRPM (Thousands)") +
ylab("resampled library\nRPM (Thousands)") +
scale_colour_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 10, line_size = 0.5) +
theme(aspect.ratio = 1) +
global_plot_theme
unnorm_umi_plt <- ggplot(resampled_metadat,
aes(og_total_umis / 1e3, total_umis / 1e3, colour = resampled)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
coord_fixed() +
facet_wrap(~library, nrow = 1) +
xlab("original library\nUMI count (Thousands)") +
ylab("resampled library\nUMI count (Thousands)") +
scale_colour_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 10, line_size = .5) +
global_plot_theme
norm_umi_plt <- ggplot(resampled_metadat,
aes(og_norm_total_umis / 1e3, norm_total_umis / 1e3, colour = resampled)) +
geom_point(size = 0.5) +
geom_abline(slope = 1) +
facet_wrap(~library, nrow = 1) +
xlab("original library\nUMI normalized RPM (Thousands)") +
ylab("resampled library\nUMIs per Million (Thousands)") +
scale_colour_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 10, line_size = 0.5) +
theme(aspect.ratio = 1) +
global_plot_theme
plt <- plot_grid(unnorm_plt, norm_plt, unnorm_umi_plt, norm_umi_plt,
labels = "AUTO",
align = 'hv')
plt
save_plot("reads_per_barcode_scatterplots.pdf", plt, base_width = 8 )Plot enrichment of umis
umi_plots <- map(split(resampled_metadat, resampled_metadat$library),
function(x){
ggplot(x,
aes(og_total_umis, norm_umi_proportion, colour = resampled)) +
geom_point(size = 0.5) +
geom_hline(aes(yintercept = 0),
linetype ="dashed",
color = "darkgrey") +
scale_x_continuous(labels = scales::comma) +
labs(x = "Abundance in original\nlibrary (UMIs)",
y = expression(paste( "Log"[2], " Normalized UMIs ",
frac("resampled", "original")))) +
scale_colour_manual(name = "Resampled:", values = color_palette) +
guides(colour = guide_legend(override.aes = list(size = 5))) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(legend.position = "top",
legend.text = element_text(size = 12))})
plt <- plot_grid(plotlist = umi_plots, nrow = 1)
save_plot("umi_ratio_MA.pdf", plt,
ncol = 3, nrow = 1,
base_width = 5,
base_height = 5)ggplot(resampled_metadat, aes(resampled,
read_proportion, fill = resampled)) +
geom_boxplot(coef = Inf) +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " Reads ", frac("resampled", "original")))) +
scale_fill_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("reads_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)
ggplot(resampled_metadat, aes(resampled,
umi_proportion, fill = resampled)) +
geom_boxplot(coef = Inf) +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " UMIs ", frac("resampled", "original")))) +
scale_fill_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("umi_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)
ggplot(resampled_metadat, aes(resampled,
norm_read_proportion, fill = resampled)) +
geom_boxplot(coef = Inf) +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " normalized Reads ", frac("resampled", "original")))) +
scale_fill_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("norm_reads_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)
ggplot(resampled_metadat, aes(resampled,
norm_umi_proportion, fill = resampled)) +
geom_boxplot(coef = Inf) +
facet_wrap(~library, nrow = 1) +
xlab("Selected for Reamplification") +
ylab(expression(paste( "Log"[2], " normalized UMIs ", frac("resampled", "original")))) +
scale_fill_manual(name = "Resampled:",
values = color_palette) +
theme_cowplot(font_size = 16, line_size = 0.5) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
legend.position = "top",
legend.text = element_text(size = 12)
)
ggsave("norm_umi_ratio_per_barcode_boxplot.pdf", width = 6, height = 5)dat <- group_by(resampled_metadat, library) %>%
filter(library != "original") %>%
mutate(total_new = sum(total_reads, na.rm = T),
total_old = sum(og_total_reads, na.rm = T))
dat_group <- group_by(dat, library, resampled) %>%
summarize(total_new = sum(total_reads,
na.rm = T) / unique(total_new),
total_old = sum(og_total_reads,
na.rm = T) / unique(total_old)) %>%
gather(lib, percent_lib, -library, -resampled ) %>%
mutate(lib = factor(lib, levels = c("total_old", "total_new"),
labels = c("Original\nLibrary",
"Resampled\nLibrary")),
resampled = factor(resampled, levels = c( "TRUE", "FALSE"))) %>%
arrange(resampled)
plt <- ggplot(dat_group, aes(lib, percent_lib, fill = resampled)) +
geom_bar(stat = "identity") +
ylab("Fraction of Reads Assigned") +
scale_fill_manual(name = "Resampled:",
values = rev(color_palette)) +
facet_wrap(~library, labeller = labeller(library = lib_names)) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5),
legend.position = "top",
legend.text = element_text(size = 16),
strip.text = element_text(margin = margin(2, 2, 2, 2, "mm"))
)
save_plot("proportion_reads_all_barcode_barplot.pdf",
plt,
base_height = 7,
base_aspect_ratio = 1)
dat_group %>%
rename(Method = library) %>%
spread(lib, percent_lib) %>%
mutate(`Targeted Library Read Fold-Enrichment` = `Resampled\nLibrary` / `Original\nLibrary`) %>%
filter(resampled == T) %>%
select(Method, `Targeted Library Read Fold-Enrichment`)Compare species
add_species_counts <- function(sc_obj,
mouse_gene_prefix = "mm38::",
human_gene_prefix = "hg38::"){
## get mouse and human reads
g_ids <- rownames(sc_obj$read_matrix)
mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
mouse_reads = colSums(sc_obj$read_matrix[mouse_ids, ])
human_reads = colSums(sc_obj$read_matrix[human_ids, ])
## get mouse and human UMIs
g_ids <- rownames(sc_obj$umi_matrix)
mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
mouse_umis = colSums(sc_obj$umi_matrix[mouse_ids, ])
human_umis = colSums(sc_obj$umi_matrix[human_ids, ])
## get norm counts for reads
g_ids <- rownames(sc_obj$norm_reads)
mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
norm_human_reads <- colSums(sc_obj$norm_reads[human_ids, ])
norm_mouse_reads <- colSums(sc_obj$norm_reads[mouse_ids, ])
## get norm counts for umis
g_ids <- rownames(sc_obj$norm_umi)
mouse_ids <- str_subset(g_ids, str_c("^", mouse_gene_prefix))
human_ids <- str_subset(g_ids, str_c("^", human_gene_prefix))
norm_human_umis <- colSums(sc_obj$norm_umi[human_ids, ])
norm_mouse_umis <- colSums(sc_obj$norm_umi[mouse_ids, ])
sc_obj <- add_metadata(sc_obj, human_reads)
sc_obj <- add_metadata(sc_obj, mouse_reads)
sc_obj <- add_metadata(sc_obj, human_umis)
sc_obj <- add_metadata(sc_obj, mouse_umis)
sc_obj <- add_metadata(sc_obj, norm_human_reads)
sc_obj <- add_metadata(sc_obj, norm_mouse_reads)
sc_obj <- add_metadata(sc_obj, norm_human_umis)
sc_obj <- add_metadata(sc_obj, norm_mouse_umis)
## make sure mouse + human == total
stopifnot(all(sc_obj$meta_dat$total_reads == sc_obj$meta_dat$mouse_reads +
sc_obj$meta_dat$human_reads, na.rm = T))
stopifnot(all(sc_obj$meta_dat$total_umis == sc_obj$meta_dat$mouse_umis +
sc_obj$meta_dat$human_umis, na.rm = T))
## check floating point totals
tol <- 1e-5
reads_check <- all(abs(sc_obj$meta_dat$norm_total_reads - (sc_obj$meta_dat$norm_mouse_reads +
sc_obj$meta_dat$norm_human_reads)) <= tol, na.rm = T)
stopifnot(reads_check)
umis_check <- all(abs(sc_obj$meta_dat$norm_total_umis - (sc_obj$meta_dat$norm_mouse_umis +
sc_obj$meta_dat$norm_human_umis)) <= tol, na.rm = T)
stopifnot(umis_check)
## calculate species purity (human / human + mouse)
sc_obj$meta_dat <- mutate(sc_obj$meta_dat,
purity_reads = human_reads / (human_reads + mouse_reads),
purity_umis = human_umis / (human_umis + mouse_umis))
sc_obj
}
sc_objs <- map(sc_objs, add_species_counts)## add in metadat columns for original mouse and original human data
sc_objs <- map(sc_objs,
function(sub_dat){
og_dat <- select(sc_objs$original$meta_dat,
cell_id,
str_subset(colnames(sc_objs$original$meta_dat),
"human|mouse|purity"))
cols <- colnames(og_dat)
new_cols <- c("cell_id", str_c("og_", cols[2:length(cols)]))
colnames(og_dat) <- new_cols
sub_dat$meta_dat <- left_join(sub_dat$meta_dat,
og_dat,
by = "cell_id")
sub_dat$meta_dat <- mutate(sub_dat$meta_dat,
norm_human_read_proportion = log2( norm_human_reads /
og_norm_human_reads),
norm_human_umi_proportion = log2( norm_human_umis /
og_norm_human_umis),
norm_mouse_read_proportion = log2( norm_mouse_reads /
og_norm_mouse_reads),
norm_mouse_umi_proportion = log2( norm_mouse_umis /
og_norm_mouse_umis))
sub_dat
})
resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>%
bind_rows(.id = "library") %>%
mutate(library = factor(library,
levels = c(
"original",
"standard",
"biotinylated",
"phosphorothioate"))) %>%
arrange(resampled)read_plots <- map(split(resampled_metadat, resampled_metadat$library),
function(x){
ggplot(x,
aes(human_reads, mouse_reads,
colour = resampled)) +
geom_point(size = 0.5) +
facet_wrap(~library, nrow = 1, scales = "free",
labeller = labeller(library = lib_names)) +
xlab("Human reads") +
ylab("Mouse reads") +
scale_colour_manual(name = "Resampled:",
values = color_palette) +
scale_x_continuous(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
guides(colour = guide_legend(override.aes = list(size = 5))) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(legend.position = "top",
legend.text = element_text(size = 12))
})
plt <- plot_grid(plotlist = read_plots,
nrow = 1)
save_plot("read_scatterplot_human_mouse.pdf",
plt, ncol = length(libs), nrow = 1,
base_height = 5, base_aspect_ratio = 1)
umi_plots <- map(split(resampled_metadat, resampled_metadat$library),
function(x){
ggplot(x,
aes(human_umis,
mouse_umis,
colour = resampled)) +
geom_point(size = 0.5) +
facet_wrap(~library, nrow = 1, scales = "free",
labeller = labeller(library = lib_names)) +
xlab("Human UMIs") +
ylab("Mouse UMIs") +
scale_colour_manual(name = "Resampled:",
values = color_palette) +
scale_x_continuous(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
guides(colour = guide_legend(override.aes = list(size = 5))) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(legend.position = "top",
legend.text = element_text(size = 12))
})
plt <- plot_grid(plotlist = umi_plots,
nrow = 1)
save_plot("umi_scatterplot_human_mouse.pdf",
plt, ncol = length(libs), nrow = 1,
base_height = 5, base_aspect_ratio = 1)Genes detected
## compute per gene or per gene/umi combo enrichment
detected_molecules <- function(sc_obj, molecule = "gene"){
umis <- sc_obj$umi_matrix
if (molecule == "gene"){
human_mat <- umis[str_detect(rownames(umis), "hg38::"), ]
mouse_mat <- umis[str_detect(rownames(umis), "mm38::"), ]
n_human_genes <- colSums(human_mat > 0)
n_mouse_genes <- colSums(mouse_mat > 0)
out_mdat <- data_frame(cell_id = colnames(umis),
n_human_genes = n_human_genes,
n_mouse_genes = n_mouse_genes)
sc_obj <- add_metadata(sc_obj, out_mdat)
}
}
sc_objs <- map(sc_objs, ~detected_molecules(.x))resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>%
bind_rows(.id = "library") %>%
mutate(library = factor(library,
levels = c(
"original",
"standard",
"biotinylated",
"phosphorothioate")))
og_genes <- filter(resampled_metadat,
library == "original") %>%
dplyr::select(cell_id,
og_human_genes = n_human_genes,
og_mouse_genes = n_mouse_genes)
resampled_metadat <- left_join(resampled_metadat,
og_genes,
by = "cell_id")
ggplot(resampled_metadat, aes(og_human_genes,
n_human_genes, colour = resampled)) +
geom_point() +
ylab("resampled_genes") +
xlab("original_genes") +
scale_colour_manual(name = "Resampled:", values = color_palette) +
facet_wrap(~library, nrow = 1) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
legend.position = "top",
legend.text = element_text(size = 16)
)
ggplot(resampled_metadat, aes(og_mouse_genes,
n_mouse_genes,
colour = resampled)) +
geom_point() +
ylab("resampled_genes") +
xlab("original_genes") +
scale_colour_manual(name = "Resampled:", values = color_palette) +
facet_wrap(~library, nrow = 1) +
theme_cowplot(font_size = 16, line_size = 1) +
theme(
legend.position = "top",
legend.text = element_text(size = 16)
)
Parse out new versus previously identified genes
calc_gene_sensitivity <- function(sc_obj,
type = "umi",
mouse_gene_prefix = "mm38::",
human_gene_prefix = "hg38::"){
if (type == "umi"){
count_matrix <- sc_obj$umi_matrix
} else {
count_matrix <- sc_obj$read_matrix
}
# generate list named with barcode of each detected gene and
# respective read/umi count
genes_detected <- apply(count_matrix, 2, function(x) x[x > 0])
sc_obj$genes_detected <- genes_detected
sc_obj
}
sc_objs <- map(sc_objs, calc_gene_sensitivity)sc_objs <- map(sc_objs,
function(x){
og_genes <- sc_objs$original$genes_detected
sub_genes <- x$genes_detected
# subset list of cell barcodes to the same as the og experiment
# and also reorders the barcodes to match
sub_genes <- sub_genes[names(og_genes)]
if(length(sub_genes) != length(og_genes)){
stop("barcode lengths not the same")
}
shared_genes <- map2(sub_genes,
og_genes,
~intersect(names(.x),
names(.y)))
new_genes <- map2(sub_genes,
og_genes,
~setdiff(names(.x),
names(.y)))
not_recovered_genes <- map2(og_genes,
sub_genes,
~setdiff(names(.x),
names(.y)))
x$shared_genes <- shared_genes
x$new_genes <- new_genes
x$not_recovered_genes <- not_recovered_genes
return(x)
})
## add gene recovery info to meta data table
sc_objs <- map(sc_objs,
function(x){
shared_genes <- map2_dfr(x$shared_genes,
names(x$shared_genes),
function(x, y){
mouse <- sum(str_detect(x, "^mm38::")) ;
human <- sum(str_detect(x, "^hg38::")) ;
data_frame(cell_id = y,
mouse_shared_genes = mouse,
human_shared_genes = human,
shared_genes = mouse + human)
})
not_recovered_genes <- map2_dfr(x$not_recovered_genes,
names(x$not_recovered_genes),
function(x, y){
mouse <- sum(str_detect(x, "^mm38::")) ;
human <- sum(str_detect(x, "^hg38::")) ;
data_frame(cell_id = y,
mouse_not_recovered_genes = mouse,
human_not_recovered_genes = human,
not_recovered_genes = mouse + human)
})
new_genes <- map2_dfr(x$new_genes,
names(x$new_genes),
function(x, y){
mouse <- sum(str_detect(x, "^mm38::")) ;
human <- sum(str_detect(x, "^hg38::")) ;
data_frame(cell_id = y,
mouse_new_genes = mouse,
human_new_genes = human,
new_genes = mouse + human)
})
gene_mdata <- left_join(shared_genes,
not_recovered_genes,
by = "cell_id") %>%
left_join(., new_genes, by = "cell_id")
x <- add_metadata(x, gene_mdata)
x
})
resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>%
bind_rows(.id = "library") %>%
mutate(library = factor(library,
levels = c(
"original",
"standard",
"biotinylated",
"phosphorothioate")))genes_recovered <- resampled_metadat %>%
dplyr::filter(library != "original") %>%
dplyr::select(cell_id,
library,
resampled,
shared_genes,
not_recovered_genes,
new_genes)
genes_recovered <- gather(genes_recovered,
key = type, value = count,
-cell_id, -resampled, -library)
genes_recovered <- mutate(genes_recovered,
type = str_replace_all(type, "_", "\n"))
plt <- ggplot(genes_recovered,
aes(cell_id, count)) +
geom_point(aes(color = resampled),
size = 0.6,
alpha = 0.75) +
facet_grid(type ~ library) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
strip.text.y = element_text(size = 12,
margin = margin(0,0.2,0,0.2, "cm"))) +
scale_color_manual(values = color_palette)
plt 
save_plot("new_genes_detected.pdf", plt, base_width = 8, base_height = 8)
targeted <- genes_recovered %>%
dplyr::filter(library == "phosphorothioate",
resampled)
targetedplt_dat <- genes_recovered %>%
dplyr::filter(library != "phosphorothioate",
resampled) %>%
group_by(type) %>%
summarize(count = mean(count)) %>%
mutate(cell_id = "Not Targeted Barcodes") %>%
bind_rows(targeted, .) %>%
filter(type == "new\ngenes")
plt <- ggplot(plt_dat,
aes(cell_id, count)) +
geom_bar(aes(fill = cell_id),
stat = "identity") +
labs(y = "Newly detected genes") +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1),
legend.position = "none") +
scale_fill_brewer(palette = "Set1")
save_plot("new_genes_barplot.pdf", plt,
base_width = 3.6, base_height = 5)
## species specific
genes_recovered <- resampled_metadat %>%
dplyr::filter(library == "phosphorothioate") %>%
select(cell_id, resampled, purity_reads, ends_with("genes")) %>%
mutate(species = ifelse(purity_reads > 0.80,
"human",
ifelse(purity_reads < 0.20,
"mouse",
"mixed"))) %>%
filter(species != "mixed") %>%
mutate(shared_genes_species = ifelse(species == "human",
human_shared_genes,
mouse_shared_genes),
new_genes_species = ifelse(species == "human",
human_new_genes,
mouse_new_genes),
not_recovered_genes_species = ifelse(species == "human",
human_not_recovered_genes,
mouse_not_recovered_genes)) %>%
select(cell_id, resampled, ends_with("_species")) %>%
gather(key = type, value = count,
-cell_id, -resampled) %>%
mutate(type = str_replace(type, "_species", "") %>%
str_replace_all(., "_", "\n"))
targeted <- genes_recovered %>%
dplyr::filter(resampled)
plt_dat <- genes_recovered %>%
dplyr::filter(!resampled) %>%
group_by(type) %>%
summarize(count = mean(count)) %>%
mutate(cell_id = "Not targeted\nbarcodes\n(mean)") %>%
bind_rows(targeted, .) %>%
mutate(type = ifelse(type == "new\ngenes",
"Newly\ndetected\ngenes",
ifelse(type == "shared\ngenes",
"Previously\ndetected\ngenes",
ifelse(type == "not\nrecovered\ngenes",
"Previously\ndetected\ngenes\nnot recovered",
NA))))
plt <- ggplot(plt_dat,
aes(cell_id, count)) +
geom_bar(aes(fill = type),
stat = "identity") +
labs(y = "# of Genes") +
scale_x_discrete(labels = cell_names[[resampled_lib]]) +
scale_y_continuous(labels = scales::comma) +
scale_fill_brewer(palette = "Set1", name = "") +
guides(fill = guide_legend(override.aes = list(size = 0.25))) +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45,
hjust = 1),
legend.position = "top",
plot.margin = unit(c(5.5, 50.5, 5.5, 5.5),
"points"))
# legend.key.size = unit(0.25, "pt"))
save_plot("new_genes_species_specific_barplot.pdf", plt,
base_width = 4, base_height = 5) Plot expression per cell
MA plots
calc_ma <- function(xmat, ymat, cell = "GCAGTTAAGTGTCCAT"){
x_rn <- rownames(xmat)
y_rn <- rownames(ymat)
xmat <- log2(xmat + 1)
ymat <- log2(ymat + 1)
rownames(xmat) <- x_rn
rownames(ymat) <- y_rn
m <- rowMeans(log2(((2^ymat + 2^xmat) / 2) + 1))
a <- xmat[, cell] - ymat[, cell]
data_frame(gene = names(a),
mean_expression_log2 = m,
log2_diff = a)
}
genes_to_plot <- rownames(sc_objs[[resampled_lib]]$umi_matrix)
cols <- colnames(sc_objs[[resampled_lib]]$norm_umi)
cell_ids <- cells[[resampled_lib]]
## append genes to reference library if necessary
ref_mat <- standardize_rows(sc_objs[[resampled_lib]]$umi_matrix[, cols],
sc_objs[[reflib]]$umi_matrix[, cols])
ma_dat <- map(cell_ids,
~calc_ma(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot, cols],
ref_mat[genes_to_plot, cols],
cell = .x))
names(ma_dat) <- cell_ids
ma_dat <- bind_rows(ma_dat, .id = "cell")
plot_ma <- function(df){
n_up <- filter(df, log2_diff > 0) %>%
group_by(cell) %>%
summarize(n = n(), n = paste0("up = ", n))
n_down <- filter(df, log2_diff < 0) %>%
group_by(cell) %>%
summarize(n = n(), n = paste0("down = ", n))
if (nrow(n_down) == 0) {
n_down = data_frame(cell = df$cell %>% unique(),
n = "down = 0")
}
plt <- ggplot(df,
aes(mean_expression_log2,
log2_diff)) +
geom_hline(aes(yintercept = 0), linetype = "dashed", colour = "grey") +
geom_point(size = 0.25) +
geom_text(data = n_up,
aes(x = max(ma_dat$mean_expression_log2) * 0.66,
y = max(ma_dat$log2_diff) * 1.2,
label = n)) +
geom_text(data = n_down,
aes(x = max(ma_dat$mean_expression_log2) * 0.66,
y = min(ma_dat$log2_diff) * 1.2,
label = n)) +
facet_wrap(~cell) +
labs(x = expression(paste("Abundance (log"[2], ")")),
y = expression(paste(frac("resampled","Original"), " (log"[2], ")")))
plt
}
plt <- plot_ma(ma_dat)
plt
save_plot("per_cell_MA_plot_all_genes.pdf", plt, base_height = 6)
## Shared genes
ma_dat <- map(cell_ids,
function(x){
genes_to_plot <- sc_objs[[resampled_lib]]$shared_genes[[x]]
calc_ma(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(ma_dat) <- cell_ids
ma_dat <- bind_rows(ma_dat, .id = "cell")
plt <- plot_ma(ma_dat)
plt
save_plot("per_cell_MA_plot_shared_genes.pdf", plt, base_height = 6)
## New genes
ma_dat <- map(cell_ids,
function(x){
genes_to_plot <- sc_objs[[resampled_lib]]$new_genes[[x]]
calc_ma(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(ma_dat) <- cell_ids
ma_dat <- bind_rows(ma_dat, .id = "cell")
plt <- plot_ma(ma_dat)
plt
save_plot("per_cell_MA_plot_new_genes.pdf", plt, base_height = 6)Histograms
get_expr <- function(xmat, ymat, cell = "GCAGTTAAGTGTCCAT"){
xrows <- rownames(xmat)
xmat <- log2(xmat[, cell] + 1)
ymat <- log2(ymat[xrows, cell] + 1)
data_frame(
gene = xrows,
resampled = xmat,
original = ymat) %>%
gather(library,
Expression, -gene)
}
plot_histogram <- function(df){
ggplot(df,
aes_string("Expression")) +
geom_density(aes_string(fill = "library"),
alpha = 0.66) +
scale_fill_viridis_d(name = "") +
facet_wrap(~cell, nrow = 1) +
theme(legend.position = "top",
strip.text = element_text(size = 8))
}
expr_dat <- map(cell_ids,
function(x){
genes_to_plot <- names(sc_objs[[resampled_lib]]$genes_detected[[x]])
get_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
expressed_in_resampled_plt <- plot_histogram(expr_dat)
expressed_in_resampled_plt
expr_dat <- map(cell_ids,
function(x){
genes_to_plot <- sc_objs[[resampled_lib]]$shared_genes[[x]]
get_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
share_genes_plt <- plot_histogram(expr_dat)
share_genes_plt
expr_dat <- map(cell_ids,
function(x){
genes_to_plot <- sc_objs[[resampled_lib]]$new_genes[[x]]
get_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
new_gene_plt <- plot_histogram(expr_dat)
plts <- list(
expressed_in_resampled_plt,
share_genes_plt,
new_gene_plt
)
plt <- plot_grid(plotlist = plts, ncol = 1)
plt
save_plot("expression_histograms.pdf", plt,
ncol = 1, nrow = 3,
base_height = 4,
base_aspect_ratio = 2)scatterplots
get_paired_expr <- function(xmat, ymat, cell = "GCAGTTAAGTGTCCAT"){
xrows <- rownames(xmat)
xmat <- log2(xmat[, cell] + 1)
ymat <- log2(ymat[xrows, cell] + 1)
data_frame(
gene = xrows,
resampled = xmat,
original = ymat)
}
plot_scatter <- function(df){
ggplot(df,
aes_string("original", "resampled")) +
geom_point(size = 0.5) +
geom_abline(aes(slope = 1, intercept = 0)) +
facet_wrap(~cell, nrow = 1) +
expand_limits(x = 0, y = 0) +
coord_fixed() +
scale_x_continuous(breaks = scales::pretty_breaks(n = 5)) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 5)) +
theme(legend.position = "top",
strip.text = element_text(size = 8))
}
expr_dat <- map(cell_ids,
function(x){
genes_to_plot <- names(sc_objs[[resampled_lib]]$genes_detected[[x]])
get_paired_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
expressed_in_resampled_plt <- plot_scatter(expr_dat)
expressed_in_resampled_plt
expr_dat <- map(cell_ids,
function(x){
genes_to_plot <- sc_objs[[resampled_lib]]$shared_genes[[x]]
get_paired_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
share_genes_plt <- plot_scatter(expr_dat)
share_genes_plt
expr_dat <- map(cell_ids,
function(x){
genes_to_plot <- sc_objs[[resampled_lib]]$new_genes[[x]]
get_paired_expr(sc_objs[[resampled_lib]]$umi_matrix[genes_to_plot,
cols],
ref_mat[genes_to_plot, cols],
cell = x)
})
names(expr_dat) <- cell_ids
expr_dat <- bind_rows(expr_dat, .id = "cell")
new_gene_plt <- ggplot(expr_dat, aes(cell,resampled)) +
geom_jitter(alpha = 0.55) +
geom_violin(aes(fill = cell)) +
ylim(0, max(expr_dat$resampled) * 1.10) +
scale_fill_brewer(palette = "Set1") +
theme(axis.text.x = element_text(angle = 90,
hjust = 1, vjust = 0.5),
legend.pos = "none",
axis.title.x = element_blank())
new_gene_plt
plts <- list(
expressed_in_resampled_plt,
share_genes_plt
)
plt <- plot_grid(plotlist = plts, ncol = 1)
plt
save_plot("expression_scatterplots.pdf", plt,
ncol = 1, nrow = 3,
base_height = 4,
base_aspect_ratio = 2)
save_plot("expression_newgenes_violinplots.pdf", new_gene_plt,
base_height = 6,
base_aspect_ratio = 0.5)Parse out new verses old UMIs
compare_umis <- function(path_to_ctrl,
path_to_test,
return_summary = F){
## umi seqs should be produced by ./get_molecule_info
ctrl_umi_seqs <- read_tsv(path_to_ctrl,
col_names = c("barcode_10x",
"umi_molecule",
"count")) %>%
filter(barcode_10x != "Cell_unmatched")
test_umi_seqs <- read_tsv(path_to_test,
col_names = c("barcode_10x",
"umi_molecule",
"count")) %>%
filter(barcode_10x != "Cell_unmatched")
umi_seqs <- full_join(ctrl_umi_seqs,
test_umi_seqs,
by = c("barcode_10x", "umi_molecule"))
if (return_summary) {
umi_seqs %>%
mutate(new_umi = ifelse(is.na(count.x) & !is.na(count.y),
1L,
0L),
not_detected_umi = ifelse(!is.na(count.x) & is.na(count.y),
1L,
0L),
shared_umi = ifelse(!is.na(count.x) & !is.na(count.y),
1L,
0L)) %>%
group_by(barcode_10x) %>%
summarize(new_umis = sum(new_umi),
not_detected_umis = sum(not_detected_umi),
shared_umis = sum(shared_umi))
} else {
umi_seqs
}
}
umi_files <- file.path(data_dir, libs, "umis", "umigroups.txt.gz")
umi_summaries <- map(umi_files[2:4],
~compare_umis(umi_files[1], .x, return_summary = T))
names(umi_summaries) <- umi_files[2:4] %>%
str_split(., "/") %>%
map_chr(~.x[7])
umi_summary <- bind_rows(umi_summaries, .id = "library")
umis_recovered <- umi_summary %>%
gather(class, count, -barcode_10x, -library)
## annotate with resampled or not
libs <- c("standard", "biotinylated","phosphorothioate")
cell_annot <- data_frame(barcode_10x = cells,
library = libs,
resampled = T) %>%
unnest()
umis_recovered <- umis_recovered %>%
left_join(., cell_annot, by = c("library", "barcode_10x")) %>%
mutate(resampled = ifelse(is.na(resampled),
F,
resampled),
library = factor(library, levels = libs)) %>%
arrange(resampled)
plt <- ggplot(umis_recovered,
aes(barcode_10x, count)) +
geom_point(aes(colour = resampled),
size = 0.6,
alpha = 0.75) +
facet_grid(library ~ class) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
strip.text.y = element_text(size = 12,
margin = margin(0,0.2,0,0.2, "cm"))) +
scale_color_manual(values = color_palette)
plt 
umi_seqs <- map(umi_files[2:4],
~compare_umis(umi_files[1], .x, return_summary = F))
names(umi_seqs) <- umi_files[2:4] %>%
str_split(., "/") %>%
map_chr(~.x[7])
new_umis <- map2(umi_seqs,
split(cell_annot, cell_annot$library)[libs],
~filter(.x,
barcode_10x %in% .y$barcode_10x,
!is.na(count.y),
is.na(count.x)) %>%
separate(umi_molecule, c("seq", "genome", "gene"), sep = "::") %>%
select(-starts_with("count")))
old_umis <- map2(umi_seqs,
split(cell_annot, cell_annot$library)[libs],
~filter(.x,
barcode_10x %in% .y$barcode_10x,
!is.na(count.x),
!is.na(count.y)) %>%
separate(umi_molecule, c("seq", "genome", "gene"), sep = "::") %>%
select(-starts_with("count")))
umi_edit_dist <- map2(new_umis,
old_umis,
~left_join(.x, .y,
by = c("barcode_10x", "genome", "gene")) %>%
na.omit() %>%
mutate(ed = kentr::get_hamming_pairs(seq.x, seq.y)) %>%
group_by(barcode_10x, seq.y, genome, gene) %>%
summarize(min_ed = as.integer(min(ed))) %>%
ungroup())
umi_edit_dist <- bind_rows(umi_edit_dist,
.id = "library")
cell_names_df <- map(cell_names,
~data_frame(old_id = names(.x),
new_id = .x)) %>%
bind_rows(., .id = "library")
umi_edit_dist_plt <- left_join(umi_edit_dist,
cell_names_df,
by = c("barcode_10x" = "old_id",
"library")) %>%
rename(cell_id = new_id)
plt <- ggplot(umi_edit_dist_plt, aes(cell_id,
min_ed)) +
geom_boxplot(coef = Inf) +
facet_wrap(~library, scales = "free_x",
labeller = labeller(library = lib_names)) +
scale_y_continuous(breaks= scales::pretty_breaks()) +
labs(y = "Minimum edit distance\noriginal vs. new UMIs") +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5),
axis.title.x = element_blank())
save_plot("umi_edit_distance.pdf", plt,
base_width = 8)Original Expt
original library tSNE
library(Seurat)
mat <- sc_objs$original$umi_matrix
human_cell_ids <- sc_objs$original$meta_dat %>%
filter(og_purity_reads > 0.80) %>%
filter(str_detect(cell_id, "1Tumor|2LukGFP|3Brain|4Bone"),
!str_detect(cell_id, "2LukGFPDead")) %>%
pull(cell_id)
mat <- mat[, human_cell_ids]
mat <- mat[str_detect(rownames(mat), "^hg38::"), ]
sobj <- CreateSeuratObject(mat, names.field = 2, names.delim = "_")
new_ids <- sobj@meta.data %>%
rownames_to_column("cell") %>%
mutate(new_id = str_sub(orig.ident, 2),
new_id = ifelse(new_id == "LukGFP",
"Primary\ncells",
new_id),
new_id = ifelse(new_id == "Tumor",
"Primary\ntumor",
new_id)) %>%
select(cell, new_id) %>%
as.data.frame(.) %>%
column_to_rownames("cell")
sobj <- AddMetaData(sobj, new_ids)
sobj <- SetAllIdent(sobj, "new_id")
sobj <- NormalizeData(sobj)
sobj <- ScaleData(sobj, vars.to.regress = "nUMI")
sobj <- FindVariableGenes(sobj, do.plot = F)
sobj <- RunPCA(sobj, pc.genes = rownames(sobj@data), pcs.compute = 5, do.print = F)
sobj <- RunTSNE(sobj, dims.use = 1:5, seed.use = 20180427)sobj <- FindClusters(sobj,
dims.use = 1:5, save.SNN = T, print.output = F)cell_percents <- group_by(sobj@meta.data, new_id) %>%
summarize(n_cells = n()) %>%
mutate(total_cells = sum(n_cells),
percentage = 100 * (n_cells / total_cells)) %>%
select(-total_cells)
cell_percentstsne_dat <- GetCellEmbeddings(sobj, "tsne") %>%
as.data.frame() %>%
tibble::rownames_to_column("cell") %>%
left_join(sobj@meta.data %>%
as.data.frame() %>%
tibble::rownames_to_column("cell"),
., by = "cell") %>%
left_join(., cell_percents, by = "new_id") %>%
mutate(cell_labels_annotated = str_c(new_id,
" (",
signif(percentage, 3),
"%)"))
tsne_plt <- ggplot(tsne_dat,
aes(tSNE_1, tSNE_2)) +
geom_point(aes(color = cell_labels_annotated), size = 1) +
scale_color_brewer(palette = "Set1",
name = "") +
labs(title = "",
x = "tSNE 1",
y = "tSNE 2") +
theme_cowplot() +
guides(color = guide_legend(override.aes = list(size = 4))) +
theme(legend.pos = "right",
aspect.ratio = 1,
legend.key.size = unit(2, 'lines'))
save_plot("original_tsne.pdf", tsne_plt,
base_width = 5.5,
base_aspect_ratio = 1.5)original library tSNE supplemented with resampled barcodes
mat <- sc_objs$original$umi_matrix
human_cell_ids <- sc_objs$original$meta_dat %>%
filter(str_detect(cell_id, "1Tumor|2LukGFP|3Brain|4Bone"),
!str_detect(cell_id, "2LukGFPDead")) %>%
pull(cell_id)
mat <- mat[, human_cell_ids]
resampled_ids <- sc_objs$phosphorothioate$meta_dat %>%
filter(resampled) %>%
pull(cell_id)
resampled_mat <- sc_objs$phosphorothioate$umi_matrix[, resampled_ids]
colnames(resampled_mat) <- str_c(colnames(resampled_mat), "::", "resampled")
mat <- as.data.frame(as.matrix(mat)) %>% rownames_to_column("cell")
resampled_mat <- as.data.frame(as.matrix(resampled_mat)) %>% rownames_to_column("cell")
combined_mats <- left_join(mat, resampled_mat, by = c("cell"))
combined_mats <- as.data.frame(combined_mats) %>%
column_to_rownames("cell") %>%
as.matrix() %>%
as(., "sparseMatrix")
combined_mats[is.na(combined_mats)] <- 0
sobj <- CreateSeuratObject(combined_mats, names.field = 2, names.delim = "_")
new_ids <- sobj@meta.data %>%
rownames_to_column("cell") %>%
mutate(new_id = str_sub(orig.ident, 2),
new_id = ifelse(str_detect(new_id, "LukGFP"),
"Primary\ncells",
new_id),
new_id = ifelse(str_detect(new_id, "Tumor"),
"Primary\ntumor",
new_id),
resampled = ifelse(str_detect(cell, "resampled"),
"resampled",
"not resampled"))
resampled_cell_ids <- new_ids[new_ids$resampled == "resampled", "cell"] %>%
str_replace("::resampled", "")
## add in computed purity and other values
mdata_og <- sc_objs$phosphorothioate$meta_dat %>%
filter(cell_id %in% rownames(sobj@meta.data))
resampled_mdata <- filter(mdata_og, resampled) %>%
select(cell_id, og_purity_reads = purity_reads) %>%
mutate(cell_id = str_c(cell_id, "::resampled"))
mdata_og <- mdata_og %>%
select(cell_id, og_purity_reads) %>%
bind_rows(., resampled_mdata) %>%
as.data.frame() %>%
column_to_rownames("cell_id")
new_ids <- mutate(new_ids,
resampled = ifelse(cell %in% resampled_cell_ids,
"original cell",
resampled)) %>%
select(cell, new_id, resampled) %>%
as.data.frame(.) %>%
column_to_rownames("cell")
sobj <- AddMetaData(sobj, new_ids)
sobj <- AddMetaData(sobj, mdata_og)
sobj <- SetAllIdent(sobj, "new_id")
sobj <- NormalizeData(sobj)
sobj <- ScaleData(sobj, vars.to.regress = "nUMI")
sobj <- FindVariableGenes(sobj)
sobj <- RunPCA(sobj, pc.genes = rownames(sobj@data))
sobj <- RunTSNE(sobj, dims.use = 1:5, seed.use = 20180502)
tsne_plt <- TSNEPlot(sobj, colors.use = brewer.pal(7, "Set1")) +
labs(title = "Breast Cancer PDX") +
theme(legend.position = "bottom")
resampled_cells <- TSNEPlot(SetAllIdent(sobj,
"resampled"),
colors.use = c("lightgrey",
brewer.pal(7, "Set1")[1:2])) +
labs(title = "Breast Cancer PDX") +
theme(legend.position = "bottom")
tmp <- resampled_cells$data
og_cell_dat <- tmp[cells$phosphoro, ] %>%
rownames_to_column("cell")
resampled_cell_dat <- tmp[str_c(cells$phosphoro, "::resampled"), ] %>%
rownames_to_column("cell") %>%
mutate(cell = str_replace(cell, "::resampled", "")) %>%
select(cell, tSNE_1, tSNE_2)
cell_dat <- left_join(og_cell_dat,
resampled_cell_dat,
by = "cell",
suffix = c("", "_resampled"))
resampled_cells <- TSNEPlot(SetAllIdent(sobj,
"resampled"),
colors.use = c("lightgrey",
brewer.pal(7, "Set1")[1:2])) +
geom_segment(data = cell_dat,
aes(x = tSNE_1,
y = tSNE_2,
xend = tSNE_1_resampled,
yend = tSNE_2_resampled),
linejoin = "mitre",
arrow = arrow(length = unit(0.03, "npc"))) +
labs(title = "Breast Cancer PDX") +
theme(legend.position = "bottom")
resampled_cells_purity <- FeaturePlot(sobj,
features.plot = "og_purity_reads",
no.legend = F,
do.return = T)$og_purity_reads +
geom_segment(data = cell_dat,
aes(x = tSNE_1,
y = tSNE_2,
xend = tSNE_1_resampled,
yend = tSNE_2_resampled),
linejoin = "mitre",
arrow = arrow(length = unit(0.01, "npc"))) +
labs(title = "Breast Cancer PDX") +
theme(legend.position = "bottom")
plt <- plot_grid(tsne_plt,
resampled_cells,
resampled_cells_purity,
nrow = 1)
save_plot("resampled_tsne.pdf", plt,
nrow = 1, ncol = 3,
base_height = 5.5, base_width = 4.25)kNN analysis
Find the k-nearest neighboors in PCA and gene expression space
## use combined data from above
data.use <- GetCellEmbeddings(object = sobj,
reduction.type = "pca",
dims.use = 1:20)
## find top 10 nearest neighboors using exact search
knn <- RANN::nn2(data.use, k = 5,
searchtype = 'standard',
eps = 0)
resampled_idxs <- knn$nn.idx[str_detect(rownames(data.use), "::resampled"), ]
nn_ids <- as_data_frame(t(apply(resampled_idxs, 1,
function(x)rownames(data.use)[x])))
colnames(nn_ids) <- c("query_cell", paste0("nearest neighbor ", 1:(ncol(nn_ids) - 1)))
nn_idsSelected barcodes
sc_metadat <- map(sc_objs["original"], ~.x$meta_dat) %>%
bind_rows(.id = "library") %>%
filter(str_detect(cell_id, "1Tumor|2LukGFP|3Brain|4Bone"),
!str_detect(cell_id, "2LukGFPDead")) %>%
mutate(cell_type = str_split(cell_id, "_", simplify = T)[, 2],
cell_type = str_sub(cell_type, 2),
cell_type = ifelse(cell_type == "LukGFP",
"Primary\ncells",
cell_type),
cell_type = ifelse(cell_type == "Tumor",
"Primary\ntumor",
cell_type))
sc_metadat <- arrange(sc_metadat, resampled)
a <- ggplot(sc_metadat, aes(cell_id, total_reads)) +
geom_point(aes(color = resampled), pt.size = 0.75) +
labs(x = "Cell",
y = "Sequencing Depth") +
scale_y_log10(labels = scales::comma, breaks = c(1e3, 1e4,1e5,1e6)) +
scale_color_manual(values = color_palette) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "top")
b <- ggplot(sc_metadat, aes(cell_id, total_reads)) +
geom_tile(aes(cell_id,
y = -.1,
height = 0.05 , fill = cell_type)) +
scale_fill_brewer(palette = "Set1", name = "Cell Type") +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
axis.line = element_blank(),
legend.position = "bottom",
plot.margin=unit(c(0,0,0,0), "lines")
)
g1 <- ggplotGrob(a)
g2 <- ggplotGrob(b)
maxWidth = grid::unit.pmax(g1$widths, g2$widths)
g1$widths <- as.list(maxWidth)
g2$widths <- as.list(maxWidth)
grid::grid.newpage()
plt <- gridExtra::grid.arrange(gridExtra::arrangeGrob(g1, g2, nrow = 2 , heights=c(.9, .175)))
grid::grid.newpage()
save_plot("selected_cells.pdf", plt, base_height = 5)Species specificity
resampled_metadat <- map(sc_objs, ~.x$meta_dat) %>%
bind_rows(.id = "library") %>%
mutate(library = factor(library,
levels = unlist(libs))) %>%
arrange(resampled)
cell_names_df <- map(cell_names,
~data_frame(old_id = names(.x),
new_id = .x)) %>%
bind_rows(., .id = "library")
plt_dat <- resampled_metadat %>%
filter(resampled, library != reflib) %>%
left_join(.,
cell_names_df, by = c("cell_id" = "old_id",
"library")) %>%
select(new_id, library, contains("purity")) %>%
gather(type, purity, -new_id, -library) %>%
mutate(type = ifelse(str_detect(type, "^og"),
str_replace(type, "^og_", "Original_"),
str_c("Resampled_", type)),
library = factor(library, levels = resampled_libs)) %>%
separate(type, c("resampled", "not_import", "count_type"), sep = "_") %>%
select(-not_import)
plt_dat <- mutate(plt_dat,
count_type = ifelse(count_type == "reads",
"Reads",
"UMIs"))
plt <- ggplot(plt_dat, aes(new_id, purity)) +
geom_hline(aes(yintercept = 0.80), linetype = "dashed",
color = "grey", alpha = 0.75) +
geom_hline(aes(yintercept = 0.20), linetype = "dashed",
color = "grey", alpha = 0.75) +
geom_point(aes(color = resampled),
position = position_dodge(width = 0.5)) +
facet_wrap(count_type~library,
labeller = labeller(library = lib_names),
scales = "free_x", nrow = 1) +
scale_color_brewer(palette = "Set1", name = "") +
guides(color = guide_legend(override.aes = list(size = 5))) +
labs(x = "",
y = expression(paste( "Species Purity ",
frac("Human", "Human + Mouse")))) +
theme(axis.text.x = element_text(angle = 90,
hjust = 1,
vjust = 0.5),
legend.pos = "top")
save_plot("species_specificity.pdf", plt,
base_width = 12, base_height= 6.5)